Please use this identifier to cite or link to this item: https://doi.org/10.1136/bmjopen-2021-052735
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dc.titleVariation in coded frailty syndromes in secondary care administrative data: an international retrospective exploratory study
dc.contributor.authorSoong, John TY
dc.contributor.authorNg, Sheryl Hui-Xian
dc.contributor.authorTan, Kyle Xin Quan
dc.contributor.authorKaubryte, Jurgita
dc.contributor.authorHopper, Adrian
dc.date.accessioned2022-02-04T01:54:15Z
dc.date.available2022-02-04T01:54:15Z
dc.date.issued2022-01
dc.identifier.citationSoong, John TY, Ng, Sheryl Hui-Xian, Tan, Kyle Xin Quan, Kaubryte, Jurgita, Hopper, Adrian (2022-01). Variation in coded frailty syndromes in secondary care administrative data: an international retrospective exploratory study. BMJ Open 12 (1) : e052735-e052735. ScholarBank@NUS Repository. https://doi.org/10.1136/bmjopen-2021-052735
dc.identifier.issn20446055
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/214696
dc.description.abstract<jats:sec><jats:title>Objectives</jats:title><jats:p>Challenges with manual methodologies to identify frailty, have led to enthusiasm for utilising large-scale administrative data, particularly standardised diagnostic codes. However, concerns have been raised regarding coding reliability and variability. We aimed to quantify variation in coding frailty syndromes within standardised diagnostic code fields of an international dataset.</jats:p></jats:sec><jats:sec><jats:title>Setting</jats:title><jats:p>Pooled data from 37 hospitals in 10 countries from 2010 to 2014.</jats:p></jats:sec><jats:sec><jats:title>Participants</jats:title><jats:p>Patients ≥75 years with admission of &gt;24 hours (N=1 404 671 patient episodes).</jats:p></jats:sec><jats:sec><jats:title>Primary and secondary outcome measures</jats:title><jats:p>Frailty syndrome groups were coded in all standardised diagnostic fields by creation of a binary flag if the relevant diagnosis was present in the 12 months leading to index admission. Volume and percentages of coded frailty syndrome groups by age, gender, year and country were tabulated, and trend analysis provided in line charts. Descriptive statistics including mean, range, and coefficient of variation (CV) were calculated. Relationship to in-hospital mortality, hospital readmission and length of stay were visualised as bar charts.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The top four contributors were UK, US, Norway and Australia, which accounted for 75.4% of the volume of admissions. There were 553 595 (39.4%) patient episodes with at least one frailty syndrome group coded. The two most frequently coded frailty syndrome groups were ‘Falls and Fractures’ (N=3 36 087; 23.9%) and ‘Delirium and Dementia’ (N=221 072; 15.7%), with the lowest CV. Trend analysis revealed some coding instability over the frailty syndrome groups from 2010 to 2014. The four countries with the lowest CV for coded frailty syndrome groups were Belgium, Australia, USA and UK. There was up to twofold, fourfold and twofold variation difference for outcomes of length of stay, 30-day readmission and inpatient mortality, respectively, across the countries.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Variation in coding frequency for frailty syndromes in standardised diagnostic fields are quantified and described. Recommendations are made to account for this variation when producing risk prediction models.</jats:p></jats:sec>
dc.publisherBMJ
dc.sourceElements
dc.typeArticle
dc.date.updated2022-02-03T00:46:20Z
dc.contributor.departmentMEDICINE
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.doi10.1136/bmjopen-2021-052735
dc.description.sourcetitleBMJ Open
dc.description.volume12
dc.description.issue1
dc.description.pagee052735-e052735
dc.published.statePublished
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